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Activity Number: 337 - Causal Inference for Complex Data Challenges
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #329352 Presentation
Title: Time-Varying Survivor Average Causal Effects with Semicompeting Risks
Author(s): Leah Comment* and Fabrizia Mealli and Corwin Zigler
Companies: Harvard T.H. Chan School of Public Health and University of Florence and Harvard T.H. Chan School of Public Health
Keywords: Causal inference; principal stratification; semicompeting risks; survivor average causal effects

In semicompeting risks problems, non-terminal time-to-event outcomes such as time to hospital readmission are subject to truncation by death. Such settings are often evaluated with parameters from illness-death models, but evaluating causal treatment effects with such models is problematic due to the evolution of incompatible risk sets over time. As an alternative, the survivor average causal effect (SACE) is a principal stratum causal effect of a treatment on the non-terminal event among units that would survive regardless of the assigned treatment. Traditional SACE formulations specify a single time point past which an individual is deemed to have always survived.

We propose a new causal estimand, the time-varying SACE (TV-SACE), for non-terminal events in the semicompeting risks setting. We adopt a Bayesian estimation procedure that is anchored to parameterization of illness-death models for both treatment arms but maintains causal interpretability. We outline several frailty specifications and highlight their connection to assumptions from the principal stratification literature. The method is demonstrated with data on hospital readmission for pancreatic cancer patients.

Authors who are presenting talks have a * after their name.

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